In the GSP (Generalized Sequential Patterns) algorithm of the following document, time series data composed of many discrete elements are input and a frequent pattern is extracted from a set of the time series data.
“Mining Sequential Patterns: Generalization and Performance Improvements”, (R. Strikant and R. Agrawal, Proceedings of the 5th International Conference Extending Database Technology, 3–17, 1996.)
However, in this document, each element composing the time series data is limited to discrete data. Accordingly, in this algorithm, general text data and general numeric data can not be processed, and these data can not be unitedly processed. Furthermore, even if a frequent time series pattern is extracted, a rule included in the frequent time series pattern can not be extracted and a future event can not be predicted using the rule.
Next, in Japanese Patent Disclosure (Kokai) P2001-175735, past time series data are input; a feature quantity is extracted from the past time series data; and a regression tree is created from the feature quantity. Furthermore, by applying a feature quantity extracted from current time series data to the regression tree, a prediction value in the future event can be calculated. However, in this method, only numeric time series data is processed as an object. Accordingly, text time series data can not be processed, and a combination of the numeric data and the text data can not be unitedly processed.
Next, in Japanese Patent Disclosure (Kokai) PH6-96052, as for a plurality of time series data, a classification class is determined from one time series data and a discrete or a numeric feature quantity is determined from other time series data. By referring to the classification class and the future quantity, a future classification class can be predicted. However, in this method, a combination pattern of the discrete feature quantity and the numeric feature quantity can not be processed. Briefly, the feature quantity of one side can be only processed.
Next, in Japanese Patent Disclosure (Kokai) PH8-85949, by utilizing a heading of news as one kind of text data and numeric time series data, a regulation direction of the numeric time series data can be predicted. However, the text data is limited to news and a prediction object is limited to the direction of change of the time series data. Accordingly, if the text data and the numeric data are supplied in time series, a problem utilizing a combination of the text data and the numeric data can not be processed.
Accordingly, in the prior art, as for time series data including both the numeric data and the text data as an information element, a frequent time series pattern can not be extracted.